1. Field of the Invention
The present invention relates to the analysis of the behavior and interests of users of online networks, and more particularly to the analysis and modeling of user's interests for users of the Internet and World Wide Web.
2. Background of the Invention
In any market, customer behavior is important. This is true of traditional retail businesses, where there are well developed mechanisms for determining customer's interests. In brick-and-mortar businesses, the customers of the business can be observed by watching those customers walk through a store. Customer behavior can also be observed by tracking their purchases (e.g., through credit card purchases.) Customer observation is, in fact, an important technique used by many retail businesses. It is so important that major databases of customer behavior exist and are in continuous usage. For example, many supermarket chains have vast databases of customer behavior. Analysis of the data in such databases can be used for many purposes (e.g., inventory control, product placement, new product analysis).
Understanding customer behavior is also necessary for electronic commerce, but the techniques of observing the customer in this medium are necessarily different. The way that customers interact with an e-commerce web site is radically different from the experience of walking into a business in person and making a purchase, but many things remain the same. When Web visitors browse a web site, sometimes they buy, and sometimes they do not. Businesses are very interested in knowing why visitors buy and why they don't. So these new electronic merchants want to understand their prospects and their customers. These businesses must observe their web visitors. This observation leads to the need for modeling the interests of customers over time, the need for managing the tremendous amount of data that such modeling would entail, and the need for categorizing web content to providing for meaningful models of user interests.
Conventionally, observation of users in online systems has typically involved using user-provided information about users interests, such as surveys or forms that allow the user the identify the categories of information that are important to them. Examples of this approach include the various customizable home pages offered by search portals such as Yahoo and Excite. In these portals, users can select various predefined categories of interest, and relevant news and related data is then provided to the user. If however the user's interests change over time, the user must manually change the specified categories of interest; this is not done automatically. These sites also allow users to specify their interests with simple keywords, but again, if the interests change, the user must manually change these keywords.
Other web sites more systematically track user behavior in terms of clickthroughs and page views, and then assemble information about these activities. As the user's activity changes on this particular web site, the assembled information is updated. This approach, while capturing some aspects of change in user behavior, it typically limited to only identifying interests relative to a single web site. User behavior on other web sites does not effect the particular site's assembled information, even though such remote behavior may most accurately express the user's interests. More particularly, the analysis of user behavior is typically limited to the particular Internet domain of the server that tracks the usage. User activity at another domain is not tracked.
Further, the assembled information on such a server only expresses the user's interest without respect to potential future or past interests. That is, it does not model changing user interests over time. However, it is the change in user interest over time that is of significant value to web marketers and others attempting to deliver content to web visitors.